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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Sep 3, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Cluster-independent marker feature identification from single-cell omics data using SEMITONES.

Anna Hendrika Cornelia Vlot^1,2, Setareh Maghsudi³, Uwe Ohler^1,2,4

¹The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, Hannoversche Str. 28, 10115 Berlin, Germany.

Nucleic Acids Research

|August 1, 2022

Summary

This summary is machine-generated.

SEMITONES offers a novel cluster-free approach for identifying cell identity markers in single-cell omics. This method outperforms existing strategies for marker gene and regulatory region discovery across various datasets.

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Last Updated: Sep 3, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Cell identity marker identification is crucial for single-cell omics analysis.
Current methods often depend on potentially arbitrary cell clustering, especially for complex developmental data.
Existing strategies may require prior biological knowledge, limiting their applicability.

Purpose of the Study:

To introduce SEMITONES, a principled, cluster-free method for identifying cell identity markers.
To evaluate SEMITONES for marker gene and regulatory region identification in human hematopoietic single-cell data.
To demonstrate SEMITONES' utility in spatial transcriptomics and cell annotation.

Main Methods:

Developed SEMITONES, a novel computational framework for marker identification.
Applied SEMITONES to single-cell RNA sequencing data from the human hematopoietic system.
Utilized simulated and curated datasets for comprehensive method evaluation.

Main Results:

SEMITONES enables cluster-free identification of cell identity markers.
The method successfully identified marker genes and regulatory regions in hematopoietic single-cell data.
SEMITONES demonstrated superior performance compared to existing methods in retrieval accuracy.

Conclusions:

SEMITONES provides a robust and accurate alternative to cluster-based marker identification.
The method is applicable to diverse single-cell omics data, including spatial transcriptomics.
SEMITONES enhances cell annotation and marker discovery in complex biological systems.